Leveraging electronic health records for clinical research

A Qualitative Study of Physicians' Views on the Reuse of Electronic Health Record Data for Secondary Analysis

Electronic health records (EHRs) have become ubiquitous in clinical practice. Given the rich biomedical data captured for a large panel of patients, secondary analysis of these data for health research is also commonplace. Yet, there are many caveats to EHR data that the researchers must be aware of, such as the accuracy of and motive for documentation, and the reason for patients' visits to the clinic. The clinician-the author of the documentation-is thus central to the correct interpretation of EHR data for research purposes. In this study, I interviewed 11 physicians in various clinical specialties to bring attention to their view on the validity of research using EHR data. Qualitative, in-depth, one-on-one interviews were conducted with practicing physicians in inpatient and outpatient medicine. Content analysis using a data-driven, inductive approach to identify themes related to challenges and opportunities in the reuse of EHR data for secondary analysis generated seven themes. Themes that reflected challenges of EHRs for research included (1) audience, (2) accuracy of data, (3) availability of data, (4) documentation practices, and (5) representativeness. Themes that reflected opportunities of EHRs for research included (6) endorsement and (7) enablers. The greatest perceived barriers reflected the intended audience of the EHR, the interpretation and meaning of the data, and the quality of the data for research purposes. Physicians generally expressed more perceived challenges than opportunities in the reuse of EHR data for research purposes; however, they remained optimistic.

Exploring Differences in Intraoperative Medication Use Between African American and Non-Hispanic White Patients During General Anesthesia: Retrospective Observational Cohort Study

This study aimed to explore whether differences exist in anesthesia care providers' use of intraoperative medication between African American and non-Hispanic White patients in adult surgical patients who underwent noncardiothoracic nonobstetric surgeries with general anesthesia. A retrospective observational cohort study used electronic health records between January 1, 2018 and August 31, 2019 at a large academic health system in the southeastern United States. To evaluate the isolated impact of race on intraoperative medication use, inverse probability of treatment weighting using the propensity scores was used to balance the covariates between African American and non-Hispanic White patients. Regression analyses were then performed to evaluate the impact of race on the total dose of opioid analgesia administered, and the use of midazolam, sugammadex, antihypotensive drugs, and antihypertensive drugs. Of the 31,790 patients included in the sample, 58.9% were non-Hispanic Whites and 13.6% were African American patients. After adjusting for significant covariates, African American patients were more likely to receive midazolam premedication (p < .0001; adjusted odds ratio [aOR] = 1.17, 99.9% CI [1.06, 1.30]), and antihypertensive drugs (p = .0002; aOR = 1.15, 99.9% CI [1.02, 1.30]), and less likely to receive antihypotensive drugs (p < .0001; aOR = 0.85, 99.9% CI [0.76, 0.95]) than non-Hispanic White patients. However, we did not find significant differences in the total dose of opioid analgesia administered, or sugammadex. This study identified differences in intraoperative anesthesia care delivery between African American and non-Hispanic White patients; however, future research is needed to understand mechanisms that contribute to these differences and whether these differences are associated with patient outcomes.

American College of Rheumatology and Food and Drug Administration Summit: Summary of the Meeting May 17-18, 2022

The American College of Rheumatology and the US Food and Drug Administration co-sponsored a public meeting in May 2022 about challenges in the clinical development of drugs for rheumatoid arthritis (RA) and psoriatic arthritis (PsA), focusing on innovative clinical trial designs, outcome measures, and data collection methods. Recommendations include early dose-ranging studies and use of active comparators. Challenges and opportunities in assessing long-term safety by leveraging real-world data from electronic health records (EHRs) and claims data are discussed, along with insights from European registries and the evolving role of real-world evidence and artificial intelligence in regulatory evaluations. Endpoints for assessing disease activity and outcome measures used in RA and PsA trials are explored, emphasizing challenges in defining remission, assessing clinical response, and evaluating structural progression. The need for outcome measures that better reflect treatment targets and the potential of advanced imaging in future trials are highlighted. Challenges with placebo-controlled trials in RA are discussed and use of non-inferiority clinical trial design, in which new drugs are evaluated with active comparators, is proposed. Pragmatic trials in RA and PsA, employing decentralized approaches, are highlighted for their real-world relevance and administrative efficiencies. Strategies for identifying at-risk populations for RA and the challenges of using EHRs and insurance claims data in drug development are discussed. Registry data and digital health technologies show promise in bridging the gap between clinical trials and real-world effectiveness.

Impact of Dementia on Incidence and Severity of Postoperative Pulmonary Complications Following Hip Fracture Surgery Among Older Patients

Postoperative pulmonary complications (PPCs) are the leading cause of death following hip fracture surgery. Dementia has been identified as a PPC risk factor that complicates the clinical course. By leveraging electronic health records, this retrospective observational study evaluated the impact of dementia on the incidence and severity of PPCs, hospital length of stay, and postoperative 30-day mortality among 875 older patients (≥65 years) who underwent hip fracture surgery between October 1, 2015 and December 31, 2018 at a health system in the southeastern United States. Inverse probability of treatment weighting using propensity scores was utilized to balance confounders between patients with and without dementia to isolate the impact of dementia on PPCs. Regression analyses revealed that dementia did not have a statistically significant impact on the incidence and severity of PPCs or postoperative 30-day mortality. However, dementia significantly extended the hospital length of stay by an average of 1.37 days.

Using "Real-World Data" to Study Cleft Lip/Palate Care: An Exploration of Speech Outcomes from a Multi-Center US Learning Health Network

OBJECTIVE

To assess the ability of a cleft-specific multi-site learning health network registry to describe variations in cleft outcomes by cleft phenotypes, ages, and treatment centers. Observed variations were assessed for coherence with prior study findings.

DESIGN

Cross-sectional analysis of prospectively collected data from 2019-2022.

SETTING

Six cleft treatment centers collected data systematically during routine clinic appointments according to a standardized protocol.

PARTICIPANTS

714 English-speaking children and adolescents with non-syndromic cleft lip/palate.

INTERVENTION

Routine multidisciplinary care and systematic outcomes measurement by cleft teams.

OUTCOME MEASURES

Speech outcomes included articulatory accuracy measured by Percent Consonants Correct (PCC), velopharyngeal function measured by Velopharyngeal Competence (VPC) Rating Scale (VPC-R), intelligibility measured by caregiver-reported Intelligibility in Context Scale (ICS), and two CLEFT-Q™ surveys, in which patients rate their own speech function and level of speech distress.

RESULTS

12year-olds exhibited high median PCC scores (91-100%), high frequency of velopharyngeal competency (62.50-100%), and high median Speech Function (80-91) relative to younger peers parsed by phenotype. Patients with bilateral cleft lip, alveolus, and palate reported low PCC scores (51-91%) relative to peers at some ages and low frequency of velopharyngeal competency (26.67%) at 5 years. ICS scores ranged from 3.93-5.0 for all ages and phenotypes. Speech Function and Speech Distress were similar across phenotypes.

CONCLUSIONS

This exploration of speech outcomes demonstrates the current ability of the cleft-specific registry to support cleft research efforts as a source of "real-world" data. Further work is focused on developing robust methodology for hypothesis-driven research and causal inference.

Differences in Health Professionals' Engagement With Electronic Health Records Based on Inpatient Race and Ethnicity

Importance

US health professionals devote a large amount of effort to engaging with patients' electronic health records (EHRs) to deliver care. It is unknown whether patients with different racial and ethnic backgrounds receive equal EHR engagement.

Objective

To investigate whether there are differences in the level of health professionals' EHR engagement for hospitalized patients according to race or ethnicity during inpatient care.

Design, Setting, and Participants

This cross-sectional study analyzed EHR access log data from 2 major medical institutions, Vanderbilt University Medical Center (VUMC) and Northwestern Medicine (NW Medicine), over a 3-year period from January 1, 2018, to December 31, 2020. The study included all adult patients (aged ≥18 years) who were discharged alive after hospitalization for at least 24 hours. The data were analyzed between August 15, 2022, and March 15, 2023.

Exposures

The actions of health professionals in each patient's EHR were based on EHR access log data. Covariates included patients' demographic information, socioeconomic characteristics, and comorbidities.

Main Outcomes and Measures

The primary outcome was the quantity of EHR engagement, as defined by the average number of EHR actions performed by health professionals within a patient's EHR per hour during the patient's hospital stay. Proportional odds logistic regression was applied based on outcome quartiles.

Results

A total of 243 416 adult patients were included from VUMC (mean [SD] age, 51.7 [19.2] years; 54.9% female and 45.1% male; 14.8% Black, 4.9% Hispanic, 77.7% White, and 2.6% other races and ethnicities) and NW Medicine (mean [SD] age, 52.8 [20.6] years; 65.2% female and 34.8% male; 11.7% Black, 12.1% Hispanic, 69.2% White, and 7.0% other races and ethnicities). When combining Black, Hispanic, or other race and ethnicity patients into 1 group, these patients were significantly less likely to receive a higher amount of EHR engagement compared with White patients (adjusted odds ratios, 0.86 [95% CI, 0.83-0.88; P < .001] for VUMC and 0.90 [95% CI, 0.88-0.92; P < .001] for NW Medicine). However, a reduction in this difference was observed from 2018 to 2020.

Conclusions and Relevance

In this cross-sectional study of inpatient EHR engagement, the findings highlight differences in how health professionals distribute their efforts to patients' EHRs, as well as a method to measure these differences. Further investigations are needed to determine whether and how EHR engagement differences are correlated with health care outcomes.

Leveraging electronic health records to streamline the conduct of cardiovascular clinical trials

Conventional randomized controlled trials (RCTs) can be expensive, time intensive, and complex to conduct. Trial recruitment, participation, and data collection can burden participants and research personnel. In the past two decades, there have been rapid technological advances and an exponential growth in digitized healthcare data. Embedding RCTs, including cardiovascular outcome trials, into electronic health record systems or registries may streamline screening, consent, randomization, follow-up visits, and outcome adjudication. Moreover, wearable sensors (i.e. health and fitness trackers) provide an opportunity to collect data on cardiovascular health and risk factors in unprecedented detail and scale, while growing internet connectivity supports the collection of patient-reported outcomes. There is a pressing need to develop robust mechanisms that facilitate data capture from diverse databases and guidance to standardize data definitions. Importantly, the data collection infrastructure should be reusable to support multiple cardiovascular RCTs over time. Systems, processes, and policies will need to have sufficient flexibility to allow interoperability between different sources of data acquisition. Clinical research guidelines, ethics oversight, and regulatory requirements also need to evolve. This review highlights recent progress towards the use of routinely generated data to conduct RCTs and discusses potential solutions for ongoing barriers. There is a particular focus on methods to utilize routinely generated data for trials while complying with regional data protection laws. The discussion is supported with examples of cardiovascular outcome trials that have successfully leveraged the electronic health record, web-enabled devices or administrative databases to conduct randomized trials.

A Learning Health System Infrastructure for Precision Rehabilitation After Stroke

ABSTRACT

Functional recovery and the response to rehabilitation interventions after stroke are highly variable. Understanding this variability will promote precision rehabilitation for stroke, allowing us to deliver targeted interventions to the right person at the right time. Capitalizing on large, heterogeneous data sets, such as those generated through clinical care and housed within the electronic health record, can lead to understanding of poststroke variability. However, accessing data from the electronic health record can be challenging because of data quality, privacy concerns, and the resources required for data extraction. Therefore, creating infrastructure that overcomes these challenges and contributes to a learning health system is needed to achieve precision rehabilitation after stroke. We describe the creation of a Precision Rehabilitation Data Repository that facilitates access to systematically collected data from the electronic health record as part of a learning health system to drive precision rehabilitation. Specifically, we describe the process of (1) standardizing the documentation of functional assessments, (2) obtaining regulatory approval, (3) defining the patient cohort, and (4) extracting data for the Precision Rehabilitation Data Repository. The development of similar infrastructures at other institutions can help generate large, heterogeneous data sets to drive poststroke care toward precision rehabilitation, thereby maximizing poststroke function within an efficient healthcare system.

Utilization of a Best Practice Alert (BPA) at Point-of-Care for Recruitment into a US-Based Autism Research Study

Provider referral is one of the most influential factors in research recruitment. To ease referral burden on providers, we adapted the Best Practice Alert (BPA) in the EPIC Electronic Health Record and assessed its utility in recruiting pediatric patients with autism spectrum disorder for the national SPARK study. During a year-long surveillance, 1203 (64.0%) patients were Interested in SPARK and 223 enrolled. Another 754 participants not recruited via the BPA also enrolled; 35.5% of these participants completed their participation compared to 58.3% of BPA-referred participants. Results suggest that (a) a BPA can successfully engage providers in the study-referral process and (b) families who learn about research through their providers may be more engaged and effectively retained.

Holistic View of Autografting Patients by Percentage of Total Body Surface Area Burned: Medical Record Abstraction Integrated with Administrative Claims

Aim

This retrospective observational study provides a holistic view of the clinical and economic characteristics of inpatient treatment of patients with thermal burns undergoing autografting, by integrating real-world data (RWD) from medical records from healthcare providers (HCPs) and administrative claims.

Methods

We identified eligible patients between July 1, 2010, and November 30, 2019, from the HealthCore Integrated Research Database^® (HIRD^®) and obtained their medical records from HCPs. We abstracted data from medical records to describe patient demographics and clinical characteristics and obtained costs of treatment from claims.

Results

Two hundred patients were stratified into cohorts based on the percentage of total body surface area (%TBSA) burned: minor (< 10%), moderate (10%-24%), and major (≥ 25%). Data obtained from medical records and administrative claims were comparable to previous findings from administrative claims data. This privately insured study cohort predominantly consisted of White men. Diabetes mellitus and hypertension were frequently reported in a relatively young population. Key clinical characteristics that could influence burn treatment decisions and long-term outcomes, such as body mass index, size of autograft donor site, and mesh ratio, were frequently underdocumented in patients' medical records.

Conclusion

Evidence generated from 2 orthogonal RWD sources confirmed that patients with larger %TBSA burned required more intensive care, thereby incurring higher costs. This study highlights considerable incompleteness in many critical fields in medical records, which limits the ability to generate broader insights. More comprehensive documentation of clinical characteristics and outcomes of autografts and donor sites in the operative and medical notes is critical to appropriately evaluate their impact on outcomes of burn treatments in future research using RWD.

Implementation of a Novel Tool to Collect Milk Feeding Data on Infants in Primary Care Clinics

We aimed to capture milk feeding type in real time in a racially and socioeconomically diverse population. An electronic tool to assess milk feeding type at every medical visit for children aged 0 to 2 years was designed and incorporated into nursing workflows. The Milk Box tool was successfully added to the electronic clinical workspace of a large health system. There were eight clinics, with diverse characteristics, which incorporated the use of the Milk Box tool over 12 months. Time to 50% uptake of Milk Box varied from 3 to 5 months. Time to >80% uptake varied from 6 to 8 months. Our results show that Milk Box can be quickly incorporated into a clinical workflow when the team is given appropriate training and support. The tool also allows a primary care practice to study local breast milk consumption trends and to provide both individualized and system-level lactation support.

Considerations for observational study design: Comparing the evidence of opioid use between electronic health records and insurance claims

PURPOSE

Pharmacoepidemiology studies often use insurance claims and/or electronic health records (EHR) to capture information about medication exposure. The choice between these data sources has important implications.

METHODS

We linked EHR from a large academic health system (2015-2017) to Medicare insurance claims for patients undergoing surgery. Drug utilization was characterized based on medication order dates in the EHR, and prescription fill dates in Medicare claims. We compared opioid use documented in EHR orders to prescription claims in four time periods: 1) Baseline (182 days before surgery); 2) Perioperative period; 3) Discharge date; 4) Follow-up (90 days after surgery).

RESULTS

We identified 11 128 patients undergoing surgery. During baseline, 34.4% (EHR) versus 44.1% (claims) had evidence of opioid use, and 56.9% of all baseline use was reflected only in one data source. During the perioperative period, 78.8% (EHR) versus 47.6% (claims) had evidence of use. On the day of discharge, 59.6% (EHR) versus 45.5% (claims) had evidence of use, and 51.8% of all discharge use was reflected only in one data source. During follow-up, 4.3% (EHR) versus 10.4% (claims) were identified with prolonged opioid use following surgery with 81.4% of all prolonged use reflected only in one data source.

CONCLUSIONS

When characterizing opioid exposure, we found substantial discrepancies between EHR medication orders and prescription claims data. In all time periods assessed, most patients' use was reflected only in the EHR, or only in the claims, not both. The potential for misclassification of drug utilization must be evaluated carefully, and choice of data source may have large impacts on key study design elements.

Establishing a National Cardiovascular Disease Surveillance System in the United States Using Electronic Health Record Data: Key Strengths and Limitations

Cardiovascular disease surveillance involves quantifying the evolving population-level burden of cardiovascular outcomes and risk factors as a data-driven initial step followed by the implementation of interventional strategies designed to alleviate this burden in the target population. Despite widespread acknowledgement of its potential value, a national surveillance system dedicated specifically to cardiovascular disease does not currently exist in the United States. Routinely collected health care data such as from electronic health records (EHRs) are a possible means of achieving national surveillance. Accordingly, this article elaborates on some key strengths and limitations of using EHR data for establishing a national cardiovascular disease surveillance system. Key strengths discussed include the: (1) ubiquity of EHRs and consequent ability to create a more "national" surveillance system, (2) existence of a common data infrastructure underlying the health care enterprise with respect to data domains and the nomenclature by which these data are expressed, (3) longitudinal length and detail that define EHR data when individuals repeatedly patronize a health care organization, and (4) breadth of outcomes capable of being surveilled with EHRs. Key limitations discussed include the: (1) incomplete ascertainment of health information related to health care-seeking behavior and the disconnect of health care data generated at separate health care organizations, (2) suspect data quality resulting from the default information-gathering processes within the clinical enterprise, (3) questionable ability to surveil patients through EHRs in the absence of documented interactions, and (4) the challenge in interpreting temporal trends in health metrics, which can be obscured by changing clinical and administrative processes.

Cohort design and natural language processing to reduce bias in electronic health records research

Electronic health record (EHR) datasets are statistically powerful but are subject to ascertainment bias and missingness. Using the Mass General Brigham multi-institutional EHR, we approximated a community-based cohort by sampling patients receiving longitudinal primary care between 2001-2018 (Community Care Cohort Project [C3PO], n = 520,868). We utilized natural language processing (NLP) to recover vital signs from unstructured notes. We assessed the validity of C3PO by deploying established risk models for myocardial infarction/stroke and atrial fibrillation. We then compared C3PO to Convenience Samples including all individuals from the same EHR with complete data, but without a longitudinal primary care requirement. NLP reduced the missingness of vital signs by 31%. NLP-recovered vital signs were highly correlated with values derived from structured fields (Pearson r range 0.95-0.99). Atrial fibrillation and myocardial infarction/stroke incidence were lower and risk models were better calibrated in C3PO as opposed to the Convenience Samples (calibration error range for myocardial infarction/stroke: 0.012-0.030 in C3PO vs. 0.028-0.046 in Convenience Samples; calibration error for atrial fibrillation 0.028 in C3PO vs. 0.036 in Convenience Samples). Sampling patients receiving regular primary care and using NLP to recover missing data may reduce bias and maximize generalizability of EHR research.

Constructing Epidemiologic Cohorts from Electronic Health Record Data

In the United States, electronic health records (EHR) are increasingly being incorporated into healthcare organizations to document patient health and services rendered. EHRs serve as a vast repository of demographic, diagnostic, procedural, therapeutic, and laboratory test data generated during the routine provision of health care. The appeal of using EHR data for epidemiologic research is clear: EHRs generate large datasets on real-world patient populations in an easily retrievable form permitting the cost-efficient execution of epidemiologic studies on a wide array of topics. Constructing epidemiologic cohorts from EHR data involves as a defining feature the development of data machinery, which transforms raw EHR data into an epidemiologic dataset from which appropriate inference can be drawn. Though data machinery includes many features, the current report focuses on three aspects of machinery development of high salience to EHR-based epidemiology: (1) selecting study participants; (2) defining “baseline” and assembly of baseline characteristics; and (3) follow-up for future outcomes. For each, the defining features and unique challenges with respect to EHR-based epidemiology are discussed. An ongoing example illustrates key points. EHR-based epidemiology will become more prominent as EHR data sources continue to proliferate. Epidemiologists must continue to improve the methods of EHR-based epidemiology given the relevance of EHRs in today’s healthcare ecosystem.

Reassessing the Pediatric Dosing Recommendations for Unfractionated Heparin Using Real-World Data: a Pharmacokinetic-Pharmacodynamic Modeling Approach

Optimal pediatric dosing of unfractionated heparin (UFH) is challenging due to paucity of clinical outcome and pharmacokinetic-pharmacodynamic (PK/PD) studies in pediatrics. This study aimed to: (i) develop a PK/PD model for UFH, quantified by anti-factor Xa assay and the UFH effect measured by activated partial thromboplastin time (aPTT) (ii) evaluate pediatric UFH infusions in achieving anti-factor Xa (0.3 - 0.7 IU/mL) therapeutic target by simulations. Electronic health record data were retrospectively collected from 633 patients < 19 years old admitted to Texas Children's Hospital. The PK/PD model was developed using a 70% (training)-30% (test) data split approach. A one-compartment PK model with linear elimination adequately described the UFH PK. An allometrically scaled body weight on clearance (CL) and volume of distribution (Vd) with an age-dependent maturation function of extracellular water on Vd were the covariates identified. Comparable with literature, the typical values for CL and Vd were 3.28 L/(hr·50 kg) and 8.83 L/50 kg, respectively. A linear model adequately described the UFH-aPTT relationship with an estimated slope of 150. Simulations of the currently recommended starting infusions (28 IU/hr/kg for pediatrics < 1 year old or 20 IU/hr/kg for pediatrics > 1 year old) showed that anti-factor Xa therapeutic target was achieved only in 15.3%, 14.6%, 36.9% and 45.11% of subjects in the age groups of < 1 year, 1-6 years, 6-12 years, and 12-19 years, respectively. In conclusion, the UFH anti-factor Xa target is not achieved initially especially in young pediatrics, suggesting the need to optimize UFH dosing to achieve higher therapeutic success. This article is protected by copyright. All rights reserved.

Using Electronic Medical Record Data for Research in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 Hospital in Beijing: Cross-sectional Study

BACKGROUND

With the proliferation of electronic medical record (EMR) systems, there is an increasing interest in utilizing EMR data for medical research; yet, there is no quantitative research on EMR data utilization for medical research purposes in China.

OBJECTIVE

This study aimed to understand how and to what extent EMR data are utilized for medical research purposes in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 hospital in Beijing, China. Obstacles and issues in the utilization of EMR data were also explored to provide a foundation for the improved utilization of such data.

METHODS

For this descriptive cross-sectional study, cluster sampling from Xuanwu Hospital, one of two Stage 7 hospitals in Beijing, was conducted from 2016 to 2019. The utilization of EMR data was described as the number of requests, the proportion of requesters, and the frequency of requests per capita. Comparisons by year, professional title, and age were conducted by double-sided chi-square tests.

RESULTS

From 2016 to 2019, EMR data utilization was poor, as the proportion of requesters was 5.8% and the frequency was 0.1 times per person per year. The frequency per capita gradually slowed and older senior-level staff more frequently used EMR data compared with younger staff.

CONCLUSIONS

The value of using EMR data for research purposes is not well studied in China. More research is needed to quantify to what extent EMR data are utilized across all hospitals in Beijing and how these systems can enhance future studies. The results of this study also suggest that young doctors may be less exposed or have less reason to access such research methods.

Accuracy of Cardiovascular Trial Outcome Ascertainment and Treatment Effect Estimates from Routine Health Data: A Systematic Review and Meta-Analysis

BACKGROUND

Registry-based randomized controlled trials allow for outcome ascertainment using routine health data (RHD). While this method provides a potential solution to the rising cost and complexity of clinical trials, comparative analyses of outcome ascertainment by clinical end point committee (CEC) adjudication compared with RHD sources are sparse. Among cardiovascular trials, we set out to systematically compare the incidence of cardiovascular events and estimated randomized treatment effects ascertained from RHD versus traditional clinical evaluation and adjudication.

METHODS

We searched MEDLINE (1976 to August 2020) for studies where outcome ascertainment was performed by both RHD and CEC adjudication to compare the incidence of cardiovascular events and treatment effects. We derived ratios of hazard ratios to compare treatment effects from RHD and CEC adjudication. We pooled ratios of hazard ratios using an inverse variance random-effects meta-analysis.

RESULTS

Nine studies (1988-2020; 32 156 patients) involving 10 randomized control trials compared outcome ascertainment with RHD and CEC in patients with or at risk of cardiovascular disease. There was a high degree of agreement and interrater reliability between CEC and RHD outcome determination for all-cause mortality (agreement percentage: 98.4%-100% and κ: 0.95-1.0) and cardiovascular mortality (agreement percentage: 97.8%-99.9% and κ: 0.66-0.99). For myocardial infarction, the κ values ranged from 0.67-0.98, and for stroke the values ranged from 0.52-0.89. In contrast, the κ value for peripheral artery disease was low (κ: 0.27). There was little difference in the randomized treatment effect derived from CEC and RHD ascertainment of events based on the ratios of hazard ratio, with pooled ratios of hazard ratios ranging from 0.93 (95% CI, 0.63-1.39) for cardiovascular mortality to 1.27 (95% CI, 0.67-2.41) for stroke.

CONCLUSIONS

Clinical outcome ascertainment using retrospectively acquired RHD displayed high levels of agreement with CEC adjudication for identifying all-cause mortality and cardiovascular outcomes. Importantly, cardiovascular treatment effects in randomized control trials determined from RHD and CEC resulted in similar point estimates. Overall, our review supports the use of RHD as a potential alternative source for clinical outcome ascertainment in cardiovascular trials. Validation studies with prospectively planned linkage are warranted.

Electronic medical record-based cohort selection and direct-to-patient, targeted recruitment: early efficacy and lessons learned

OBJECTIVE

The study sought to characterize institution-wide participation in secure messaging (SM) at a large academic health network, describe our experience with electronic medical record (EMR)-based cohort selection, and discuss the potential roles of SM for research recruitment.

MATERIALS AND METHODS

Study teams defined eligibility criteria to create a computable phenotype, structured EMR data, to identify and recruit participants. Patients with SM accounts matching this phenotype received recruitment messages. We compared demographic characteristics across SM users and the overall health system. We also tabulated SM activation and use, characteristics of individual studies, and efficacy of the recruitment methods.

RESULTS

Of the 1 308 820 patients in the health network, 40% had active SM accounts. SM users had a greater proportion of white and non-Hispanic patients than nonactive SM users id. Among the studies included (n = 13), 77% recruited participants with a specific disease or condition. All studies used demographic criteria for their phenotype, while 46% (n = 6) used demographic, disease, and healthcare utilization criteria. The average SM response rate was 2.9%, with higher rates among condition-specific (3.4%) vs general health (1.4%) studies. Those studies with a more inclusive comprehensive phenotype had a higher response rate.

DISCUSSION

Target population and EMR queries (computable phenotypes) affect recruitment efficacy and should be considered when designing an EMR-based recruitment strategy.

CONCLUSIONS

SM guided by EMR-based cohort selection is a promising approach to identify and enroll research participants. Efforts to increase the number of active SM users and response rate should be implemented to enhance the effectiveness of this recruitment strategy.

Who are we missing? Underrepresentation of data sources used for pharmacoepidemiology research in the United States

PURPOSE

Research using healthcare databases often includes patients frequently excluded from clinical trials; yet it is not known whether commonly used data represents the overall population or specific sub-populations of interest. We aimed to examine population representativeness from data sources in recent research studies in the United States (US).

METHODS

We identified data sources from abstracts accepted to the 34th International Conference on Pharmacoepidemiology & Therapeutic Risk Management. The final sample included research studies using ≥1 data source from the US. We classified data sources broadly as claims, linkage, electronic health records (EHR), survey, distributed data network, and other. Studies using claims and EHRs were further classified into more specific categories, including special populations of interest (eg, children).

RESULTS

We identified 356 abstracts. The majority used claims data (n = 201, 56.5%), followed by data linkages (n = 46, 12.9%), and EHR data (n = 39, 11.0%). Among EHR studies, most (n = 16, 41.0%) came from network data sources (eg, Kaiser Permanente). Almost half (49.4%) of claims-based studies used commercial claims data sources, followed by Medicare (22.1%), Medicaid (11.3%), and Medicare Supplemental (6.1%). Only 15% of studies included children in the study population (n = 53), with 8% focused on a pediatric topic (n = 27).

CONCLUSIONS

We find that certain populations in the US are under-represented in pharmacoepidemiology, particularly Medicaid enrollees and children. Researchers should strive to utilize data sources that may be more representative of the US population, particularly vulnerable populations.

Understanding the use of observational and randomized data in cardiovascular medicine

The availability of large datasets from multiple sources [e.g. registries, biobanks, electronic health records (EHRs), claims or billing databases, implantable devices, wearable sensors, and mobile apps], coupled with advances in computing and analytic technologies, have provided new opportunities for conducting innovative health research. Equally, improved digital access to health information has facilitated the conduct of efficient randomized controlled trials (RCTs) upon which clinical management decisions can be based, for instance, by permitting the identification of eligible patients for recruitment and/or linkage for follow-up via their EHRs. Given these advances in cardiovascular data science and the complexities they behold, it is important that health professionals have clarity on the appropriate use and interpretation of observational, so-called 'real-world', and randomized data in cardiovascular medicine. The Cardiovascular Roundtable of the European Society of Cardiology (ESC) held a workshop to explore the future of RCTs and the current and emerging opportunities for gathering and exploiting complex observational datasets in cardiovascular research. The aim of this article is to provide a perspective on the appropriate use of randomized and observational data and to outline the ESC plans for supporting the collection and availability of clinical data to monitor and improve the quality of care of patients with cardiovascular disease in Europe and provide an infrastructure for undertaking pragmatic RCTs. Moreover, the ESC continues to campaign for greater engagement amongst regulators, industry, patients, and health professionals in the development and application of a more efficient regulatory framework that is able to take maximal advantage of new opportunities for improving the design and efficiency of observational studies and RCT in patients with cardiovascular disease.

Rates and Predictors of Patient Underreporting of Hospitalizations During Follow-Up After Acute Myocardial Infarction: An Assessment From the TRIUMPH Study

BACKGROUND

Many clinical investigations depend on participant self-report as a principal method of identifying health care events. If self-report is used as the trigger to collect and adjudicate medical records, any event that is not reported by the patient will be missed by the investigators, reducing the power of the study and misrepresenting the risk of its participants. We sought to determine the rates and predictors of underreporting hospitalization events during the follow-up period of a prospective study of patients hospitalized with an acute myocardial infarction.

METHODS AND RESULTS

The TRIUMPH (Translational Research Investigating Underlying Disparities in Acute Myocardial Infarction Patients' Health Status) registry, a longitudinal multicenter cohort study of people with acute myocardial infarction in the United States, queried patients for hospitalization events during interviews at 1, 6, and 12 months. To validate these self-reports, medical records for all events at every hospital where the patient reported receiving care were acquired for adjudication, not just those for the reported events. Of the 4340 participants in TRIUMPH, 1209 (28%) reported at least one hospitalization. After medical records abstraction and adjudication, we identified 1086 hospitalizations from 639 participants (60.2±12 years of age, 38.2% women). Of these hospitalizations, 346 (31.9%) were underreported by the participants. Rates of underreporting ranged from 22.5% to 55.6% based on different patient characteristics. The odds of underreporting were highest for those not currently working (adjusted odds ratio, 1.66 [95% CI, 1.04-2.63]), lowest for those married (adjusted odds ratio, 0.50 [95% CI, 0.33-0.76]), and increased the longer the elapsed time between the admission and the patient's follow-up interview (adjusted odds ratio per month, 1.16 [95% CI, 1.08-1.24]). There was a substantial within-individual variation on the accuracy of reporting.

CONCLUSIONS

Hospitalizations after acute myocardial infarction are commonly underreported in interviews and should not be used alone to determine event rates in clinical studies.

Cause-specific mortality of type B aortic dissection and assessment of competing risks of mortality

OBJECTIVE

Natural history studies of type B aortic dissection (TBAD) commonly report all-cause mortality. Our aim was to determine cause-specific mortality in TBAD and to evaluate the clinical characteristics associated with aorta-related and nonaorta-related mortality.

METHODS

Clinical and administrative records were reviewed for patients with acute TBAD between 1995 and 2017. Demographics, comorbidities, presentation, and initial imaging findings were abstracted. Cause of death was ascertained through a multimodality approach using electronic health records, obituaries, social media, Social Security Death Index, and state mortality records. Causes of death were classified as aorta related, nonaorta related, or unknown. A Fine-Gray multivariate competing risk regression model for subdistribution hazard ratio was employed to analyze the association of clinical characteristics with aorta-related and nonaorta-related mortality.

RESULTS

A total of 275 individuals met inclusion criteria (61.1 ± 13.7 years, 70.9% male, 68% white). Mean survival after discharge was 6.3 ± 4.7 years. Completeness of follow-up Clark C index was 0.87. All-cause mortality was 50.2% (n = 138; mean age, 70.1 ± 14.6 years) including an in-hospital mortality of 8.4%. Cause-specific mortality was aorta related, nonaorta related, and unknown in 51%, 43%, and 6%, respectively. Compared with patients with nonaorta-related mortality, patients with aorta-related mortality were younger at acute TBAD (69.5 ± 11.2 years vs 61.6 ± 15.5 years; P = .001), underwent more descending thoracic aortic repairs (19.4% vs 45.8%; P = .002), and had a shorter survival duration (5.7 ± 3.9 vs 3.4 ± 4.5 years; P = .002). There was clear variation in cause of death by each decade of life, with higher aorta-related mortality among those younger than 50 years and older than 70 years and a stepwise increase in nonaorta-related mortality with each increasing decade (P < .001). All-cause mortality at 1 year, 3 years, and 10 years was 15%, 24%, and 57%, respectively. After accounting for competing risks, the cumulative incidence of aorta-related mortality at 1 year, 3 years, and 10 years was 8.9%, 16.5%, and 27.2%, respectively, and that of nonaorta-related mortality was 2.7%, 7.2%, and 29%, respectively. A maximum descending thoracic aortic diameter >4 cm was associated with an increase in hazard of aorta-related mortality by 84% (subdistribution hazard ratio, 1.84; 95% confidence interval, 1.03-3.28) on multivariate competing risk regression analysis.

CONCLUSIONS

TBAD is associated with high 10-year mortality. Those at risk for aorta-related mortality have a clinical phenotype different from that of individuals at risk for nonaorta-related mortality. This information is important for building risk prediction models that account for competing mortality risks and to direct optimal and individualized surgical and medical management of TBAD.

Development of Patient Databases for Endocrinological Clinical and Pharmaceutical Trials: A Survey

BACKGROUND

According to European legislation, a clinical trial is a research involving patients, which also includes a research end-product. The main objective of the clinical trial is to prove that the research product, i.e. a proposed medication or treatment, is effective and safe for patients. The implementation, development, and operation of a patient database, which will function as a matrix of samples with the appropriate parameterization, may provide appropriate tools to generate samples for clinical trials.

AIMS

The aim of the present work is to review the literature with respect to the up-to-date progress on the development of databases for clinical trials and patient recruitment using free and open-source software in the field of endocrinology.

METHODS

An electronic literature search was conducted by the authors from 1984 to June 2019. Original articles and systematic reviews selected, and the titles and abstracts of papers screened to determine whether they met the eligibility criteria, and full texts of the selected articles were retrieved.

RESULTS

The present review has indicated that the electronic health records are related with both the patient recruitment and the decision support systems in the domain of endocrinology. The free and open-source software provides integrated solutions concerning electronic health records, patient recruitment, and the decision support systems.

CONCLUSION

The patient recruitment relates closely to the electronic health record. There is maturity at the academic and research level, which may lead to good practices for the deployment of the electronic health record in selecting the right patients for clinical trials.

Antibiotic Review Kit for Hospitals (ARK-Hospital): study protocol for a stepped-wedge cluster-randomised controlled trial

BACKGROUND

To ensure patients continue to get early access to antibiotics at admission, while also safely reducing antibiotic use in hospitals, one needs to target the continued need for antibiotics as more diagnostic information becomes available. UK Department of Health guidance promotes an initiative called 'Start Smart then Focus': early effective antibiotics followed by active 'review and revision' 24-72 h later. However in 2017, < 10% of antibiotic prescriptions were discontinued at review, despite studies suggesting that 20-30% of prescriptions could be stopped safely.

METHODS/DESIGN

Antibiotic Review Kit for Hospitals (ARK-Hospital) is a complex 'review and revise' behavioural intervention targeting healthcare professionals involved in antibiotic prescribing or administration in inpatients admitted to acute/general medicine (the largest consumers of non-prophylactic antibiotics in hospitals). The primary study objective is to evaluate whether ARK-Hospital can safely reduce the total antibiotic burden in acute/general medical inpatients by at least 15%. The primary hypotheses are therefore that the introduction of the behavioural intervention will be non-inferior in terms of 30-day mortality post-admission (relative margin 5%) for an acute/general medical inpatient, and superior in terms of defined daily doses of antibiotics per acute/general medical admission (co-primary outcomes). The unit of observation is a hospital organisation, a single hospital or group of hospitals organised with one executive board and governance framework (National Health Service trusts in England; health boards in Northern Ireland, Wales and Scotland). The study comprises a feasibility study in one organisation (phase I), an internal pilot trial in three organisations (phase II) and a cluster (organisation)-randomised stepped-wedge trial (phase III) targeting a minimum of 36 organisations in total. Randomisation will occur over 18 months from November 2017 with a further 12 months follow-up to assess sustainability. The behavioural intervention will be delivered to healthcare professionals involved in antibiotic prescribing or administration in adult inpatients admitted to acute/general medicine. Outcomes will be assessed in adult inpatients admitted to acute/general medicine, collected through routine electronic health records in all patients.

DISCUSSION

ARK-Hospital aims to provide a feasible, sustainable and generalisable mechanism for increasing antibiotic stopping in patients who no longer need to receive them at 'review and revise'.

TRIAL REGISTRATION

ISRCTN Current Controlled Trials, ISRCTN12674243 . Registered on 10 April 2017.

Development of electronic medical records for clinical and research purposes: the breast cancer module using an implementation framework in a middle income country- Malaysia

BACKGROUND

Advances in medical domain has led to an increase of clinical data production which offers enhancement opportunities for clinical research sector. In this paper, we propose to expand the scope of Electronic Medical Records in the University Malaya Medical Center (UMMC) using different techniques in establishing interoperability functions between multiple clinical departments involving diagnosis, screening and treatment of breast cancer and building automatic systems for clinical audits as well as for potential data mining to enhance clinical breast cancer research in the future.

RESULTS

Quality Implementation Framework (QIF) was adopted to develop the breast cancer module as part of the in-house EMR system used at UMMC, called i-Pesakit©. The completion of the i-Pesakit© Breast Cancer Module requires management of clinical data electronically, integration of clinical data from multiple internal clinical departments towards setting up of a research focused patient data governance model. The 14 QIF steps were performed in four main phases involved in this study which are (i) initial considerations regarding host setting, (ii) creating structure for implementation, (iii) ongoing structure once implementation begins, and (iv) improving future applications. The architectural framework of the module incorporates both clinical and research needs that comply to the Personal Data Protection Act.

CONCLUSION

The completion of the UMMC i-Pesakit© Breast Cancer Module required populating EMR including management of clinical data access, establishing information technology and research focused governance model and integrating clinical data from multiple internal clinical departments. This multidisciplinary collaboration has enhanced the quality of data capture in clinical service, benefited hospital data monitoring, quality assurance, audit reporting and research data management, as well as a framework for implementing a responsive EMR for a clinical and research organization in a typical middle-income country setting. Future applications include establishing integration with external organization such as the National Registration Department for mortality data, reporting of institutional data for national cancer registry as well as data mining for clinical research. We believe that integration of multiple clinical visit data sources provides a more comprehensive, accurate and real-time update of clinical data to be used for epidemiological studies and audits.